A bridge between measurement and meaning

ThinkFeel.Live is built on research from interoception science, heart rate variability studies, and emotion granularity theory. This page outlines the evidence base and explains how these findings inform the application's design.

Scientific research with brain waves and data visualization

Interoception: Sensing from Within

Interoception refers to the perception of internal bodily states—heartbeat, breath, temperature, tension. Research suggests that interoceptive awareness correlates with emotional intelligence, decision-making quality, and psychological well-being.

Importantly, interoceptive accuracy can be trained. Studies show that focused attention on physical sensations, combined with external feedback, improves bodily awareness over time.

ThinkFeel.Live's body scanner translates this research into practice. The application guides attention to specific body regions, captures sensation reports, and over time reveals patterns that might otherwise remain invisible.

Key Research

  • Critchley & Garfinkel (2017). Interoception and emotion. Current Opinion in Psychology.
  • Füstös et al. (2013). On the embodiment of emotion regulation. Social Cognitive and Affective Neuroscience.
  • Mehling et al. (2012). The Multidimensional Assessment of Interoceptive Awareness. PLOS ONE.

Heart Rate Variability: The Coherence Signal

Heart rate variability (HRV) measures the variation in time between consecutive heartbeats. Higher HRV generally indicates greater autonomic flexibility—the capacity to adapt to stress and return to baseline.

The relationship between HRV and emotional states is bidirectional. Emotional dysregulation reduces HRV, while practices that improve HRV (particularly resonant breathing at approximately 6 breaths per minute) tend to improve emotional regulation.

ThinkFeel.Live integrates with Apple Watch to monitor HRV in real time. The breathing guide paces respiration to each user's optimal coherence frequency, providing biofeedback that makes the invisible visible.

Key Research

  • McCraty & Zayas (2014). Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being. Frontiers in Psychology.
  • Lehrer & Gevirtz (2014). Heart rate variability biofeedback: How and why does it work? Frontiers in Psychology.
  • Thayer et al. (2012). A meta-analysis of heart rate variability and neuroimaging studies. Neuroscience & Biobehavioral Reviews.

Emotion Granularity: The Vocabulary Effect

Emotion granularity refers to the ability to make fine-grained distinctions between emotional states. Rather than experiencing "bad," a person with high emotion granularity might distinguish between frustrated, disappointed, anxious, and overwhelmed.

Research demonstrates that higher emotion granularity predicts better emotion regulation, fewer maladaptive coping behaviors, and improved psychological outcomes. The relationship appears to be causal—training in emotional vocabulary tends to produce corresponding improvements in emotional functioning.

ThinkFeel.Live's emotion trainer introduces vocabulary progressively, linking new words to physical sensations and contexts. The approach respects that emotions are learned—and can be learned more precisely.

Key Research

  • Barrett (2017). How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin Harcourt.
  • Kashdan et al. (2015). Unpacking emotion differentiation: Transforming unpleasant experience. Current Directions in Psychological Science.
  • Smidt & Suvak (2015). A brief, but nuanced, review of emotional granularity and emotion differentiation research. Current Opinion in Psychology.

Alexithymia: The Condition We Address

Alexithymia—from the Greek for "no words for emotions"—describes difficulty identifying and describing feelings. It affects approximately 10% of the general population and 25% of psychiatric patients.

Alexithymia is dimensional rather than categorical. Most people fall somewhere on a spectrum, with higher scores correlating with reduced emotional awareness, difficulty distinguishing emotions from bodily sensations, and challenges in interpersonal relationships.

ThinkFeel.Live is designed specifically for the alexithymic experience. Rather than assuming emotional literacy as a starting point, the application begins with what is observable—physical sensation and physiological measurement—and builds toward emotional vocabulary over time.

Key Research

  • Taylor et al. (1997). Disorders of Affect Regulation: Alexithymia in Medical and Psychiatric Illness. Cambridge University Press.
  • Lumley et al. (2007). Alexithymia and health: Current status and future directions. Journal of Psychosomatic Research.
  • Bagby et al. (2020). The twenty-item Toronto Alexithymia Scale: Convergent, discriminant, and concurrent validity. Journal of Psychosomatic Research.

Design implications

The research informs specific design decisions:

Somatic primacy

The application begins with body sensation rather than emotion labels, respecting the alexithymic experience where physical awareness often precedes—or substitutes for—emotional awareness.

Biofeedback loops

Real-time HRV monitoring provides external validation of internal states, building the interoceptive confidence that underlies emotional recognition.

Progressive vocabulary

Emotion words are introduced gradually and contextually, linked to physiological signatures and situational patterns rather than presented as abstract categories.

Non-judgmental framework

The interface avoids success/failure framing. There is no right way to feel, only clearer and less clear perception of what is present.

Limitations

ThinkFeel.Live is a self-directed training tool, not a medical device or therapeutic intervention. The application does not diagnose conditions, treat disorders, or replace professional care.

The research base, while substantial, continues to evolve. We commit to updating our approach as evidence develops, and to transparency about what is known and what remains uncertain.

Individual responses to interoceptive and HRV training vary. The application tracks each user's progress relative to their own baseline rather than external norms.